School of Information Science and Technology, Donghua University, Shanghai, China.
Chaos. 2011 Jun;21(2):025114. doi: 10.1063/1.3595701.
In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.
本文通过同时最小化控制成本和收敛速度的优化，研究了混沌系统的多目标同步。通过一种改进的多目标进化方法，包括混合染色体表示，对耦合形式和耦合强度进行了优化。混合编码方案将二进制表示与实数表示相结合。通过将多目标同步转换为多目标约束问题，也考虑了耦合形式的约束。此外，通过在混沌或超混沌状态下的 Rössler 系统和时滞混沌神经网络，分析和验证了自适应学习方法和非支配排序遗传算法-II 的性能以及所提出方法的有效性和贡献。
